detailed analysis of ecmwf surface pressure data e. fagiolini 1, t. schmidt 1, g. schwarz 2, l....
TRANSCRIPT
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Detailed Analysis of ECMWF Surface Pressure Data
E. Fagiolini1, T. Schmidt1, G. Schwarz2, L. Zenner3
(1) GFZ Department 1, Potsdam, Germany(2) DLR IMF, Oberpfaffenhofen, Germany
(3) TUM IAPG, Munich, Germany
EGU 2012, Vienna, Austria
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• Project Background
• Selected Surface Pressure Data
• Basic Data Characteristics
• Spatial Neighbourhoods
• Temporal Neighbourhoods
• Outlook
Summary of this Presentation
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• Priority Programme ”Mass transport and Mass distribution in the System Earth” of the German Research Foundation (DFG).
• Project IDEAL-GRACE: ”Improved De-Aliasing for Gravity Field Modelling with GRACE”Partners: TUM IAPG, GFZ, DLR IMF, UHH IfM.
• An improved gravity field modelling requires computation of atmospheric contributions (mainly surface pressure).
• How good are available surface pressure data?
Project Background
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Selected Surface Pressure Data
• We selected ECMWF surface pressure data from operational analysis,, global coverage, every 6 hours, N48 Gauss grid (192 columns, 96 lines), 29% land / 71% water.
• ECMWF surface pressure = combination of predictive modelling and measurements.
• Time period: 1 year from Sep. 1, 2007 to Aug. 31, 2008366 days, every 6 hours => 1464 data sets.
• Footprint per sample: about 200 * 200 km.
• A few outliers had to be removed by interpolation.
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Median Values of 00, 06, 12 and 18 Hour Data
Small differences around Indonesia.
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Europe Asia Pacific Ocean N. America Atlantic Ocean
Pressure Profile Along +45 deg. N
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Histogram of Global 1 Year Surface Pressure Levels
Mountain areas
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Contrast Enhanced Dynamic Pressure Range
Equatorial Regions are quiet
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Diurnal Variation in the Tropics
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Surface Pressure Time Series Data
Horizontal axis: transect along -30°S
Vertical axis: time (1 year, midnight data)
Principal motion of the pressure areas.
Sept.
Febr.
Aug.
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Time Series Data
Horizontal axis: transect along -60°S
Vertical axis: time (1 year, midnight data)
Pressure data Low pass data High pass data
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Horizontal axis: time (1 year, midnight data)
Vertical axis: transect along the zero meridian
Zero Meridian Transects versus Time (Contrast Enhanced)
North Pole
South Pole
Equator
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Total Variation of 6 Hour Pressure Levels
Longitude-dependent total variation: interpolated time step effects?
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Contrast Enhanced Entropy of Pressure Levels
The information content is not constant.
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Histogram of East/West 1 Sample Differences
Back-to-back Laplacians visible small fluctuations and noise.
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Histogram of North/South 1 Sample Differences
Broader the surface pressure differences are direction-dependent.
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Sum of 2 Gaussians two overlapping effects.
Histogram of East/West 2 Sample Differences
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Histogram of North/South 2 Sample Differences
Again, direction dependence.
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Histogram of East/West 16 Sample Differences
Single Gaussian we see different effects.
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Histogram of North/South 16 Sample Differences
the longitude dependence becomes smaller.
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Pressure < 990 hPa above Water
The east coast shores of the continents show rather low pressure values. Why?
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Differences Between 6 h Time Steps above Land
Single Gaussian very regular distributions above land.
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Differences Between 6 h Time Steps above Water
Double peak distribution above water. Due to the microwave instruments?
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Distribution of 6 h Biases above Water at 00h
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Distribution of 6 h Biases above Water at 06h
The bias locations have changed. Is it a sensor effect, or is it physical reality?
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Conclusion and Outlook
• ECMWF data describe a large variety of atmospheric effects in the spatial and temporal domain and at various scales.
• Outlier correction and removal of topographic background allows detailed studies of temporal phenomena (e.g., transects).
• Many open questions.
• A further topic to be discussed is the analysis of estimated errors provided by ECMWF (current activities).
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Thank you!
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Backslides
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Contrast Enhanced Positive Skewness of Pressure Levels
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Contrast Enhanced Negative Skewness of Pressure Levels